With the ongoing developments in laser scanning technology, applications for describing tunnel deformation using rich point cloud data have become a significant topic of investigation. This study describes the independently developed a mobile tunnel monitoring system called the second version of Tunnel Scan developed by Capital Normal University (CNU-TS-2) for data acquisition, which has an electric system to control its forward speed and is compatible with various laser scanners such as the Faro and Leica models. A comparison with corresponding data acquired by total station data demonstrates that the data collected by CNU-TS-2 is accurate. Following data acquisition, the overall and local deformation of the tunnel is determined by denoising and 360° deformation analysis of the point cloud data. To enhance the expression of the analysis results, this study proposes an expansion of the tunnel point cloud data into a two-dimensional image via cylindrical projection, followed by an expression of the tunnel deformation through color difference to visualize the deformation. Compared with the three-dimensional modeling method of visualization, this method is easier to implement and facilitates storage. In addition, it is conducive to the performance of comprehensive analysis of problems such as water leakage in the tunnel, thereby achieving the effect of multiple uses for a single image.

Tunnel dislocation affects the stability and waterproof of the structure and endangers its service life. This paper presents a dislocation calculation method of shield tunnel based on cross sectional point clouds, which contain three main steps. First, a longitudinal joint detection method is proposed based on the circumferential joints detected by the existing gradient accumulation method. The point clouds containing capping block in each ring are converted into gray image to obtain the center line position of the block, and the longitudinal joints are calculated through combining with the tunnel design data. Second, tunnel appendages are removed quickly through unfolding the tunnel point clouds by cylinder projection and adjusting the parameters of the cloth simulation filtering algorithm. Finally, the circumferential dislocations are calculated by selecting multiple denoised sections on both sides of the joint and clustering the points at each angle. Meanwhile, the longitudinal dislocations are calculated through fitting the segments in each ring separately. Experimental results show that the automatic extraction rate of longitudinal joints is higher than 94%. The point clouds filtering method can quickly separate the very long tunnel lining from the close appendages attached to it. Meanwhile, the RMSE of the repeated circumferential and longitudinal dislocation are evaluated to be 1.56 mm and 0.57 mm respectively. Compared with the existing state of art methods, these values reach 1.75 mm and 0.63 mm. Through the method proposed, the dislocation value at any mileage and any angle of tunnel can be displayed intuitively.

Efficient, high-precision, and automatic measurement of tunnel structural changes is the key to ensuring the safe operation of subways. Conventional manual, static, and discrete measurements cannot meet the requirements of rapid and full-section detection in subway construction and operation. Mobile laser scanning technology is the primary method for tunnel detection. Herein, we propose a method to calculate shield tunnel displacements of a full cross-section tunnel. The point cloud data, obtained via a mobile tunnel deformation detection system, were fitted, projected, and interpolated to generate an orthophoto image. Combined with the cumulative characteristics of the tunnel gray gradient, the longitudinal ring seam of the tunnel was identified, while the Canny algorithm and Hough line detection algorithm identified the transverse seam. The symmetrical vertical foot method and cross-section superposition analysis were used to calculate the circumferential and radial displacements, respectively. The proposed displacement calculation method achieves automatic recognition of a ring seam, reduces human–computer interaction, and is fast, intelligent, and accurate. Furthermore, the description of the tunnel deformation location and deformation amount is more quantitative and specific. These results confirm the significance of shield tunnel displacement monitoring based on mobile monitoring systems in tunnel disease monitoring.

A rapid, precise and automated means for the regular inspection and maintenance of a large number of tunnels is needed. Based on the depth study of the tunnel monitoring method, the CNU-TS-1 mobile tunnel monitoring system (TS1) is developed and presented. It can efficiently obtain the cross-sections that are orthogonal to the tunnel in a dynamic way, and the control measurements that depend on design data are eliminated. By using odometers to locate the cross-sections and correcting the data based on longitudinal joints of tunnel segment lining, the cost of the system has been significantly reduced, and the interval between adjacent cross-sections can reach 1–2 cm when pushed to collect data at a normal walking speed. Meanwhile, the relative deformation of tunnel can be analyzed by selecting cross-sections from original data. Through the measurement of the actual tunnel, the applicability of the system for tunnel deformation detection is verified, and the system is shown to be 15 times more efficient than that of the total station. The simulation experiment of the tunnel deformation indicates that the measurement accuracy of TS1 for cross-sections is 1.1 mm. Compared with the traditional method, TS1 improves the efficiency as well as increases the density of the obtained points.

Keywords: Subway Tunnel, Deformation Monitoring, Point Clouds, Central Axis, Cross Section, Random Sample Consensus, K- Neighbors An automated method for tunnel deformation monitoring using high density point clouds data is presented. Firstly, the 3D point clouds data are converted to two-dimensional surface by projection on the XOY plane, the projection point set of central axis on XOY plane named U_xoy is calculated by combining the Alpha Shape algorithm with RANSAC (Random Sampling Consistency) algorithm, and then the projection point set of central axis on YOZ plane named Uyoz is obtained by highest and lowest points which are extracted by intersecting straight lines that through each point of U_xoy and perpendicular to the two -dimensional surface with the tunnel point clouds, U_xoy and U_yoz together form the 3D center axis finally. Secondly, the buffer of each cross section is calculated by K-Nearest neighbor algorithm, and the initial cross-sectional point set is quickly constructed by projection method. Finally, the cross sections are denoised and the section lines are fitted using the method of iterative ellipse fitting. In order to improve the accuracy of the cross section, a fine adjustment method is proposed to rotate the initial sectional plane around the intercept point in the horizontal and vertical direction within the buffer. The proposed method is used in Shanghai subway tunnel, and the deformation of each section in the direction of 0 to 360 degrees is calculated. The result shows that the cross sections becomes flat circles from regular circles due to the great pressure at the top of the tunnel